Priority Watersheds for Wetland Conservation

Priority Watershed Analysis Methodology

This analysis identifies priority watersheds for wetland conservation based on a composite score that integrates multiple conservation criteria. We use HydroBASINS Level 6 watersheds as our spatial units of analysis.

Data Sources

The analysis integrates the following global datasets:

Wetlands: Global Lakes and Wetlands Database (GLWD) - provides wetland extent and type
Carbon Storage: Vulnerable carbon data from Conservation International - represents irrecoverable carbon stocks
Protected Areas: World Database on Protected Areas (WDPA) - identifies existing conservation coverage
Biodiversity Value: Nature’s Contributions to People (NCP) scores - biodiversity and ecosystem service importance
Watersheds: HydroBASINS Level 6 - watershed boundaries for analysis units

Composite Score Calculation

Each watershed receives a composite score (0-1 scale) based on four equally-weighted criteria. Scores are normalized within each country to identify relative conservation priorities within that country, not for cross-country comparison.

1. Wetland Area (25% weight)

Metric: Total hectares of wetlands within the watershed
Normalization: Scaled relative to the largest wetland area in the country
Rationale: Larger wetland areas provide greater ecosystem services and habitat

2. Vulnerable Carbon (25% weight)

Metric: Total vulnerable carbon stocks (metric tons) in wetland areas
Normalization: Scaled relative to the highest carbon stock in the country
Rationale: Identifies wetlands whose loss would release significant greenhouse gases

3. Protection Gap (25% weight)

Metric: Fraction of wetland area NOT currently protected (1 - protected_fraction)
Range: 0 (fully protected) to 1 (completely unprotected)
Rationale: Prioritizes watersheds with low current protection status

4. Biodiversity & Ecosystem Services (25% weight)

Metric: Average Nature’s Contributions to People (NCP) score
Range: 0 (low importance) to 1 (high importance)
Rationale: Identifies areas critical for biodiversity and human well-being

Formula

composite_score = (
    (wetland_area / max_wetland_area) * 0.25 +
    (total_carbon / max_total_carbon) * 0.25 +
    (1 - protected_fraction) * 0.25 +
    avg_ncp_score * 0.25
)

Important Notes

GLWD Multiple Categories: A single hexagon in GLWD can have multiple wetland type codes. We use n_distinct(h8) to avoid counting the same location multiple times.
H3 Hexagons: All data is indexed using H3 hexagons at resolution 8, where each hex = 73.73 hectares
Country-Specific Normalization: Scores are normalized within each country to identify relative priorities within that country
Regional Context: Each basin is associated with its administrative region for spatial orientation
Output Files: Analysis produces both summary files (top 10 basins) and complete files (all basins) for each country

Setup

Code

library(duckdbfs)
library(dplyr)
library(dbplyr)
library(ggplot2)
library(tidyr)
library(patchwork)

# Configure DuckDB connection to S3
duckdb_secrets(
    key = "",
    secret = "",
    endpoint = "minio.carlboettiger.info"
)

[1] 1

Code

# H3 hexagon area constant
hex_hectares <- 73.7327598

# Load datasets
countries <- open_dataset(
    "s3://public-overturemaps/hex/countries.parquet",
    recursive = FALSE
)
regions <- open_dataset("s3://public-overturemaps/hex/regions/**")
wetlands <- open_dataset("s3://public-wetlands/glwd/hex/**")
hydrobasins <- open_dataset("s3://public-hydrobasins/level_06/hexes/**")
carbon <- open_dataset("s3://public-carbon/hex/vulnerable-carbon/**")
wdpa <- open_dataset("s3://public-wdpa/hex/**")
ncp <- open_dataset("s3://public-ncp/hex/ncp_biod_nathab/**")

Analysis Functions

Code

# Calculate basin metrics for a country
calculate_basin_metrics <- function(country_code) {
    basin_wetlands <- countries |>
        filter(country == country_code) |>
        inner_join(
            wetlands,
            by = c("h8", "h0"),
            suffix = c("_country", "_wetland")
        ) |>
        filter(Z > 0) |>
        inner_join(hydrobasins, by = c("h8", "h0"), suffix = c("", "_basin")) |>
        left_join(
            regions |> select(h8, h0, region_name = name),
            by = c("h8", "h0")
        ) |>
        select(
            basin_id = id_basin,
            PFAF_ID,
            UP_AREA,
            SUB_AREA,
            h8,
            h0,
            region_name
        )

    basin_metrics <- basin_wetlands |>
        left_join(carbon, by = c("h8", "h0")) |>
        left_join(
            wdpa |> select(h8, h0, wdpa_present = OBJECTID),
            by = c("h8", "h0")
        ) |>
        left_join(ncp, by = c("h8", "h0")) |>
        group_by(basin_id, PFAF_ID, UP_AREA, SUB_AREA, region_name) |>
        summarise(
            wetland_hex_count = n_distinct(h8),
            total_carbon = round(coalesce(sum(carbon, na.rm = TRUE), 0), 2),
            protected_fraction = round(
                n_distinct(h8[!is.na(wdpa_present)]) / n_distinct(h8),
                3
            ),
            avg_ncp_score = round(mean(ncp, na.rm = TRUE), 3),
            .groups = "drop"
        ) |>
        mutate(
            wetland_area_hectares = round(wetland_hex_count * hex_hectares, 2)
        )

    all_basins <- basin_metrics |>
        mutate(
            norm_wetland_area = wetland_area_hectares /
                max(wetland_area_hectares, na.rm = TRUE),
            norm_carbon = total_carbon / max(total_carbon, na.rm = TRUE),
            protection_gap = 1 - protected_fraction,
            composite_score = round(
                (norm_wetland_area *
                    0.25 +
                    norm_carbon * 0.25 +
                    protection_gap * 0.25 +
                    coalesce(avg_ncp_score, 0) * 0.25),
                3
            )
        ) |>
        select(
            basin_id,
            PFAF_ID,
            region_name,
            upstream_area_km2 = UP_AREA,
            basin_area_km2 = SUB_AREA,
            wetland_hex_count,
            wetland_area_hectares,
            total_carbon,
            protected_fraction,
            avg_ncp_score,
            norm_wetland_area,
            norm_carbon,
            protection_gap,
            composite_score
        ) |>
        filter(wetland_hex_count > 0) |>
        arrange(desc(composite_score)) |>
        collect()

    return(all_basins)
}

# Save basin results to CSV
save_basin_results <- function(basin_data, country_code, country_name) {
    filename <- paste0(
        'priority_hydrobasins_all_',
        tolower(country_code),
        '.csv'
    )
    basin_data |>
        mutate(
            country = country_name,
            country_code = country_code
        ) |>
        write.csv(filename, row.names = FALSE)

    invisible(filename)
}

# Create country visualization
plot_country_analysis <- function(basin_data, country_name, top_n = 10) {
    top_basins <- basin_data |> head(top_n)

    plot_data <- top_basins |>
        mutate(
            rank = row_number(),
            basin_label = paste0("#", rank, ": ", region_name)
        ) |>
        arrange(rank)

    # A. Composite Score by Basin
    p1 <- plot_data |>
        ggplot(aes(x = reorder(basin_label, -rank), y = composite_score)) +
        geom_col(fill = "#66c2a5") +
        coord_flip() +
        labs(
            title = paste("A. Top", top_n, "Priority Basins"),
            x = NULL,
            y = "Composite Score"
        ) +
        theme_minimal() +
        theme(panel.grid.major.y = element_blank())

    # B. Component Scores Breakdown
    p2 <- plot_data |>
        select(
            basin_label,
            rank,
            `Wetland Area` = norm_wetland_area,
            `Carbon Storage` = norm_carbon,
            `Protection Gap` = protection_gap,
            `NCP Score` = avg_ncp_score
        ) |>
        pivot_longer(
            cols = c(
                `Wetland Area`,
                `Carbon Storage`,
                `Protection Gap`,
                `NCP Score`
            ),
            names_to = "metric",
            values_to = "score"
        ) |>
        ggplot(aes(x = reorder(basin_label, -rank), y = score, fill = metric)) +
        geom_col(position = "dodge") +
        coord_flip() +
        scale_fill_manual(
            values = c(
                "Wetland Area" = "#8dd3c7",
                "Carbon Storage" = "#bebada",
                "Protection Gap" = "#fb8072",
                "NCP Score" = "#80b1d3"
            )
        ) +
        labs(
            title = "B. Conservation Criteria Breakdown",
            x = NULL,
            y = "Normalized Score",
            fill = "Metric"
        ) +
        theme_minimal() +
        theme(panel.grid.major.y = element_blank())

    # C. Distribution of all basins with top 10 highlighted
    p3 <- basin_data |>
        mutate(is_top10 = basin_id %in% top_basins$basin_id) |>
        ggplot(aes(x = composite_score, fill = is_top10)) +
        geom_histogram(bins = 30, alpha = 0.8) +
        scale_fill_manual(
            values = c("TRUE" = "#fc8d62", "FALSE" = "#e5e5e5"),
            labels = c("TRUE" = "Top 10", "FALSE" = "Other basins")
        ) +
        labs(
            title = "C. Distribution of All Basin Scores",
            x = "Composite Score",
            y = "Number of Basins",
            fill = NULL
        ) +
        theme_minimal()

    # D. Wetland Area vs Protection Status
    p4 <- plot_data |>
        ggplot(aes(
            x = wetland_area_hectares,
            y = protected_fraction,
            size = total_carbon,
            color = avg_ncp_score
        )) +
        geom_point(alpha = 0.7) +
        scale_size_continuous(range = c(3, 10)) +
        scale_color_gradient(low = "#fee5d9", high = "#a50f15") +
        labs(
            title = "D. Wetland Area vs Protection Status",
            x = "Wetland Area (hectares)",
            y = "Protected Fraction",
            size = "Carbon",
            color = "NCP Score"
        ) +
        theme_minimal()

    # Combine all plots
    (p1 | p2) /
        (p3 | p4) +
        plot_annotation(
            title = paste("Priority Watershed Analysis:", country_name),
            subtitle = paste(
                "Top",
                top_n,
                "of",
                nrow(basin_data),
                "total basins with wetlands"
            )
        )
}

North America: United States, Canada, and Mexico

United States

Code

us_basins <- calculate_basin_metrics('US')
save_basin_results(us_basins, 'US', 'United States')

Code

us_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 1: Top 10 priority basins in the United States

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
7060044360	Florida	988240.2	104702831	0.49	0.85	0.69
7060043860	Florida	2073733.9	22062072	0.06	0.65	0.66
7060057980	Alaska	1760664.6	41228180	0.13	0.60	0.64
7060468870	South Dakota	2534711.1	19305736	0.12	0.37	0.61
7060015100	Washington	1185844.0	38421206	0.20	0.78	0.60
7060044350	Florida	837604.2	36521213	0.12	0.83	0.60
7060356500	North Dakota	1359263.4	8219300	0.04	0.80	0.60
7060392650	Michigan	2242803.1	28089297	0.24	0.44	0.59
7060715640	Mississippi	2433033.6	15481275	0.07	0.29	0.58
7060044770	Florida	1787724.5	13163445	0.07	0.56	0.58

Code

plot_country_analysis(us_basins, "United States")

Figure 1: Priority watershed analysis for the United States

Canada

Code

canada_basins <- calculate_basin_metrics('CA')
save_basin_results(canada_basins, 'CA', 'Canada')

Code

canada_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 2: Top 10 priority basins in Canada

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
7060276840	Ontario	4645532.5	77840679	0.06	0.11	0.75
7060022280	Manitoba	1557604.6	67077695	0.21	0.90	0.72
7060023960	Ontario	1589530.8	70165077	0.00	0.57	0.70
7060022280	Ontario	954839.2	40191987	0.00	0.76	0.62
8060155650	Northwest Territories	2939946.3	45848079	0.05	0.34	0.62
8060231560	Northwest Territories	2661236.5	63675499	0.24	0.34	0.61
7060400750	Ontario	2321402.2	32024168	0.09	0.60	0.60
7060151610	Ontario	1033807.0	38739962	0.00	0.62	0.58
7060055930	British Columbia	1464775.0	35363397	0.17	0.73	0.58
7060021690	Manitoba	1214599.8	43298777	0.09	0.60	0.58

Code

plot_country_analysis(canada_basins, "Canada")

Figure 2: Priority watershed analysis for Canada

Mexico

Code

mexico_basins <- calculate_basin_metrics('MX')
save_basin_results(mexico_basins, 'MX', 'Mexico')

Code

mexico_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 3: Top 10 priority basins in Mexico

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
7060051220	Quintana Roo	2500720.28	45117925	0.30	0.74	0.86
7060051220	Yucatán	1287226.52	16061017	0.19	0.76	0.61
7060852120	Puebla	22488.49	175746	0.00	1.05	0.52
7060004990	Guerrero	122691.31	664136	0.01	1.00	0.52
7060794890	Sinaloa	13935.49	33172	0.00	1.05	0.51
7060006740	Durango	49990.81	454582	0.00	1.03	0.51
7060005740	Colima	15852.54	137601	0.00	1.05	0.51
7060848300	Oaxaca	108903.29	868167	0.02	1.01	0.51
7060738830	Chihuahua	8036.87	41	0.01	1.05	0.51
7060075070	Yucatán	779281.54	7545089	0.05	0.62	0.51

Code

plot_country_analysis(mexico_basins, "Mexico")

Figure 3: Priority watershed analysis for Mexico

Asia: China, South Korea, and Thailand

China

Code

china_basins <- calculate_basin_metrics('CN')
save_basin_results(china_basins, 'CN', 'China')

Code

china_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 4: Top 10 priority basins in China

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
4060154710	Heilongjiang	2987356	22955778	0.09	0.16	0.77
4060213920	Heilongjiang	2134563	17984046	0.01	0.54	0.76
4060780120	Sichuan	1875835	11361170	0.11	0.65	0.67
4060047500	Hainan Province	2368960	5731888	0.00	0.61	0.66
4060094870	Heilongjiang	1667688	12468012	0.00	0.43	0.63
4060765990	Sichuan	2129623	4655770	0.01	0.60	0.63
4060780100	Sichuan	1056590	6680384	0.06	0.84	0.60
4060784170	Tibet	1177438	8057633	0.00	0.65	0.60
4060239010	Heilongjiang	2376776	7869675	0.00	0.23	0.59
4060152570	Inner Mongolia	1436314	13308502	0.00	0.27	0.58

Code

plot_country_analysis(china_basins, "China")

Figure 4: Priority watershed analysis for China

South Korea

Code

korea_basins <- calculate_basin_metrics('KR')
save_basin_results(korea_basins, 'KR', 'South Korea')

Code

korea_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 5: Top 10 priority basins in South Korea

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
4060005050	Gangwon State	661898.98	2719734	0.25	0.88	0.86
4060003940	North Gyeongsang	829641.01	1266503	0.09	0.18	0.64
4060004110	South Jeolla	668018.80	1000731	0.33	0.60	0.61
4060004690	South Chungcheong	573493.41	368187	0.04	0.62	0.60
4060004680	North Chungcheong	289990.94	641605	0.27	0.81	0.53
4060005050	Gyeonggi	417991.02	691925	0.45	0.80	0.53
4060005050	North Chungcheong	272147.62	846176	0.14	0.58	0.52
4060003940	South Gyeongsang	488110.87	696291	0.08	0.26	0.51
4060003940	Busan	2285.72	3110	0.03	1.05	0.51
4060004690	Seoul	1843.32	507	0.04	1.05	0.50

Code

plot_country_analysis(korea_basins, "South Korea")

Figure 5: Priority watershed analysis for South Korea

Thailand

Code

thailand_basins <- calculate_basin_metrics('TH')
save_basin_results(thailand_basins, 'TH', 'Thailand')

Code

thailand_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 6: Top 10 priority basins in Thailand

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
4060021590	Phang-nga Province	245235.2	4624243	0.19	0.76	0.74
4060018990	Nakhon Si Thammarat Province	505216.9	2442041	0.15	0.76	0.74
4060019420	Yala Province	340203.0	3516882	0.15	0.72	0.72
4060018970	Surat Thani Province	514728.4	3331241	0.37	0.68	0.71
4060018360	Kanchanaburi Province	625769.9	3349343	0.50	0.57	0.70
4060019420	Narathiwat Province	371097.0	3205501	0.20	0.71	0.70
4060018230	Chachoengsao Province	371391.9	518250	0.06	0.81	0.62
4060021590	Krabi Province	185364.2	2643575	0.12	0.69	0.61
4060021590	Ranong Province	171354.9	2801321	0.36	0.90	0.61
4060018230	Prachin Buri Province	330101.6	1368072	0.14	0.72	0.60

Code

plot_country_analysis(thailand_basins, "Thailand")

Figure 6: Priority watershed analysis for Thailand

Europe: United Kingdom, France, and Spain

United Kingdom

Code

uk_basins <- calculate_basin_metrics('GB')
save_basin_results(uk_basins, 'GB', 'United Kingdom')

Code

uk_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 7: Top 10 priority basins in the United Kingdom

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
2060051160	Scotland	3736112.67	54955700	0.38	0.70	0.83
2060050790	Scotland	972166.44	20513463	0.12	0.75	0.56
2060049690	Wales	1164535.21	25987594	0.41	0.69	0.52
2060050420	England	845862.22	24518796	0.57	0.92	0.51
2060053240	England	673991.16	20112092	0.42	0.90	0.51
2060052960	England	842912.91	20521267	0.39	0.77	0.49
2060055600	Northern Ireland	487004.88	13689548	0.23	0.78	0.48
2060055310	Northern Ireland	204387.21	6189826	0.13	0.85	0.47
2060051150	Scotland	323981.75	7635554	0.07	0.70	0.46
2060050790	England	38193.57	852933	0.07	0.90	0.46

Code

plot_country_analysis(uk_basins, "United Kingdom")

Figure 7: Priority watershed analysis for the United Kingdom

France

Code

france_basins <- calculate_basin_metrics('FR')
save_basin_results(france_basins, 'FR', 'France')

Code

france_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 8: Top 10 priority basins in France

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
2060552470	Auvergne-Rhône-Alpes	523502.6	3630486	0.34	0.54	0.66
2060536370	Auvergne-Rhône-Alpes	570249.2	3036506	0.30	0.50	0.65
2060021230	Normandy	262488.6	5501654	0.38	0.58	0.65
2060021230	Brittany	684682.4	2205511	0.21	0.38	0.64
2060420240	Grand Est	544295.2	3653752	0.29	0.23	0.60
2060502920	Nouvelle-Aquitaine	557862.1	2238149	0.16	0.32	0.60
2060569180	Occitania	418802.1	1736915	0.28	0.62	0.57
2060021030	Pays de la Loire	390783.6	1268552	0.13	0.55	0.56
2060497340	Pays de la Loire	450064.8	2084031	0.11	0.26	0.55
2060510690	Bourgogne – Franche-Comté	333567.0	3552314	0.39	0.45	0.55

Code

plot_country_analysis(france_basins, "France")

Figure 8: Priority watershed analysis for France

Spain

Code

spain_basins <- calculate_basin_metrics('ES')
save_basin_results(spain_basins, 'ES', 'Spain')

Code

spain_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 9: Top 10 priority basins in Spain

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
2060018360	Andalusia	814230.87	6299718	0.23	0.85	0.91
2060615620	Aragon	330765.16	2286648	0.28	0.67	0.54
2060609250	Aragon	172239.73	1230820	0.24	0.97	0.53
2060018240	Andalusia	378396.52	1758187	0.24	0.56	0.52
2060613120	Castile and León	366894.21	1091214	0.18	0.53	0.49
2060017700	Andalusia	392995.61	903264	0.23	0.55	0.49
2060615620	Catalonia	250986.31	3179844	0.36	0.47	0.48
2060689480	Castile-La Mancha	1032.26	7840	0.14	1.05	0.48
2060019420	Galicia	2728.11	14942	0.11	1.00	0.48
2060598480	Aragon	102046.14	976468	0.28	0.89	0.47

Code

plot_country_analysis(spain_basins, "Spain")

Figure 9: Priority watershed analysis for Spain

South America: Brazil and Chile

Brazil

Code

brazil_basins <- calculate_basin_metrics('BR')
save_basin_results(brazil_basins, 'BR', 'Brazil')

Code

brazil_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 10: Top 10 priority basins in Brazil

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
6060294360	Amazonas	4883173	141682590	0.57	0.84	0.73
6060269510	Amazonas	4911339	148601298	0.61	0.70	0.70
6060287830	Amazonas	3724537	181332932	0.62	0.78	0.70
6060286190	Amazonas	4049698	108787314	0.47	0.82	0.67
6060305980	Amazonas	2601808	69408761	0.00	0.77	0.66
6060267240	Amazonas	3240923	120317611	0.59	0.86	0.63
6060280410	Amazonas	1969918	51386243	0.05	0.83	0.61
6060360870	Amazonas	3137919	88687485	0.33	0.69	0.60
6060247260	Amazonas	2710932	210340274	0.96	0.79	0.59
6060692440	Mato Grosso do Sul	2165531	31779711	0.02	0.74	0.58

Code

plot_country_analysis(brazil_basins, "Brazil")

Figure 10: Priority watershed analysis for Brazil

Chile

Code

chile_basins <- calculate_basin_metrics('CL')
save_basin_results(chile_basins, 'CL', 'Chile')

Code

chile_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 11: Top 10 priority basins in Chile

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
6060036450	Magallanes and Chilean Antarctica Region	2508167.29	50305952	0.29	0.63	0.84
6060024060	Aysen del General Carlos Ibanez del Campo Region	1866028.69	40006604	0.65	0.44	0.58
6060025140	Aysen del General Carlos Ibanez del Campo Region	1281032.97	29289888	0.36	0.58	0.58
6060022920	Magallanes and Chilean Antarctica Region	1907540.23	44721318	0.94	0.56	0.57
6060039730	Los Lagos Region	739834.51	11770384	0.06	0.77	0.56
6060025130	Aysen del General Carlos Ibanez del Campo Region	1090360.05	22264746	0.15	0.48	0.55
6060021950	Magallanes and Chilean Antarctica Region	1602212.87	33232005	0.73	0.56	0.53
6060026390	Los Ríos	26322.60	337804	0.00	1.05	0.52
6060038660	Magallanes and Chilean Antarctica Region	1210691.92	41324289	0.90	0.63	0.51
6060026390	Araucania Region	31852.55	438122	0.00	1.02	0.51

Code

plot_country_analysis(chile_basins, "Chile")

Figure 11: Priority watershed analysis for Chile

Australia

Code

australia_basins <- calculate_basin_metrics('AU')
save_basin_results(australia_basins, 'AU', 'Australia')

Code

australia_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 12: Top 10 priority basins in Australia

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
5060077790	Tasmania	2510379.27	95835397	0.51	0.82	0.83
5060456070	South Australia	577917.37	2485184	0.02	0.98	0.55
5060049720	Queensland	1511521.58	20521613	0.35	0.67	0.54
5060352950	Western Australia	73364.10	278040	0.00	1.05	0.52
5060405540	Western Australia	6857.15	0	0.01	1.05	0.51
5060284090	Northern Territory	4571.43	33459	0.02	1.05	0.51
5060373470	Western Australia	4202.77	641	0.02	1.05	0.51
5060590940	New South Wales	5751.16	67171	0.01	1.04	0.51
5060591290	New South Wales	24921.67	261349	0.00	1.02	0.51
5060057550	Western Australia	7963.14	55137	0.03	1.05	0.51

Code

plot_country_analysis(australia_basins, "Australia")

Figure 12: Priority watershed analysis for Australia

India

Code

india_basins <- calculate_basin_metrics('IN')
save_basin_results(india_basins, 'IN', 'India')

Code

india_basins |>
    head(10) |>
    select(
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 13: Top 10 priority basins in India

basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
4060838850	Arunachal Pradesh	1023558.2	16138869	0.00	0.80	0.78
4060838850	South Tibet	988240.2	15930818	0.00	0.80	0.78
4060048470	Andaman and Nicobar Islands	519742.2	14082290	0.00	0.91	0.74
4060949680	Meghalaya	758046.5	8537129	0.00	0.93	0.68
4060835610	Arunachal Pradesh	835097.2	13006787	0.00	0.63	0.68
4060835610	South Tibet	786138.7	12501877	0.00	0.61	0.66
4060032880	Gujarat	1669014.8	1348407	0.00	0.76	0.60
4060029870	Karnataka	1233770.3	5971331	0.04	0.64	0.59
4060836200	Arunachal Pradesh	514802.1	7857501	0.00	0.67	0.58
4060836200	South Tibet	514802.1	7857501	0.00	0.67	0.58

Code

plot_country_analysis(india_basins, "India")

Figure 13: Priority watershed analysis for India

Summary: Combined Results

Code

all_results <- bind_rows(
    us_basins |>
        head(10) |>
        mutate(country = "United States", country_code = "US"),
    canada_basins |>
        head(10) |>
        mutate(country = "Canada", country_code = "CA"),
    mexico_basins |>
        head(10) |>
        mutate(country = "Mexico", country_code = "MX"),
    china_basins |> head(10) |> mutate(country = "China", country_code = "CN"),
    korea_basins |>
        head(10) |>
        mutate(country = "South Korea", country_code = "KR"),
    thailand_basins |>
        head(10) |>
        mutate(country = "Thailand", country_code = "TH"),
    uk_basins |>
        head(10) |>
        mutate(country = "United Kingdom", country_code = "GB"),
    france_basins |>
        head(10) |>
        mutate(country = "France", country_code = "FR"),
    spain_basins |> head(10) |> mutate(country = "Spain", country_code = "ES"),
    brazil_basins |>
        head(10) |>
        mutate(country = "Brazil", country_code = "BR"),
    chile_basins |> head(10) |> mutate(country = "Chile", country_code = "CL"),
    australia_basins |>
        head(10) |>
        mutate(country = "Australia", country_code = "AU"),
    india_basins |> head(10) |> mutate(country = "India", country_code = "IN")
)

write.csv(
    all_results,
    'priority_hydrobasins_top10_summary.csv',
    row.names = FALSE
)

Code

all_results |>
    select(
        country,
        basin_id,
        region_name,
        wetland_area_hectares,
        total_carbon,
        protected_fraction,
        avg_ncp_score,
        composite_score
    ) |>
    knitr::kable(digits = 2)

Table 14: Top 10 priority basins per country

country	basin_id	region_name	wetland_area_hectares	total_carbon	protected_fraction	avg_ncp_score	composite_score
United States	7060044360	Florida	988240.18	104702831	0.49	0.85	0.69
United States	7060043860	Florida	2073733.87	22062072	0.06	0.65	0.66
United States	7060057980	Alaska	1760664.57	41228180	0.13	0.60	0.64
United States	7060468870	South Dakota	2534711.08	19305736	0.12	0.37	0.61
United States	7060015100	Washington	1185843.98	38421206	0.20	0.78	0.60
United States	7060044350	Florida	837604.15	36521213	0.12	0.83	0.60
United States	7060356500	North Dakota	1359263.43	8219300	0.04	0.80	0.60
United States	7060392650	Michigan	2242803.09	28089297	0.24	0.44	0.59
United States	7060715640	Mississippi	2433033.61	15481275	0.07	0.29	0.58
United States	7060044770	Florida	1787724.49	13163445	0.07	0.56	0.58
Canada	7060276840	Ontario	4645532.53	77840679	0.06	0.11	0.75
Canada	7060022280	Manitoba	1557604.55	67077695	0.21	0.90	0.72
Canada	7060023960	Ontario	1589530.84	70165077	0.00	0.57	0.70
Canada	7060022280	Ontario	954839.24	40191987	0.00	0.76	0.62
Canada	8060155650	Northwest Territories	2939946.33	45848079	0.05	0.34	0.62
Canada	8060231560	Northwest Territories	2661236.50	63675499	0.24	0.34	0.61
Canada	7060400750	Ontario	2321402.21	32024168	0.09	0.60	0.60
Canada	7060151610	Ontario	1033807.03	38739962	0.00	0.62	0.58
Canada	7060055930	British Columbia	1464775.01	35363397	0.17	0.73	0.58
Canada	7060021690	Manitoba	1214599.75	43298777	0.09	0.60	0.58
Mexico	7060051220	Quintana Roo	2500720.28	45117925	0.30	0.74	0.86
Mexico	7060051220	Yucatán	1287226.52	16061017	0.19	0.76	0.61
Mexico	7060852120	Puebla	22488.49	175746	0.00	1.05	0.52
Mexico	7060004990	Guerrero	122691.31	664136	0.01	1.00	0.52
Mexico	7060794890	Sinaloa	13935.49	33172	0.00	1.05	0.51
Mexico	7060006740	Durango	49990.81	454582	0.00	1.03	0.51
Mexico	7060005740	Colima	15852.54	137601	0.00	1.05	0.51
Mexico	7060848300	Oaxaca	108903.29	868167	0.02	1.01	0.51
Mexico	7060738830	Chihuahua	8036.87	41	0.01	1.05	0.51
Mexico	7060075070	Yucatán	779281.54	7545089	0.05	0.62	0.51
China	4060154710	Heilongjiang	2987356.50	22955778	0.09	0.16	0.77
China	4060213920	Heilongjiang	2134563.40	17984046	0.01	0.54	0.76
China	4060780120	Sichuan	1875835.14	11361170	0.11	0.65	0.67
China	4060047500	Hainan Province	2368959.84	5731888	0.00	0.61	0.66
China	4060094870	Heilongjiang	1667687.56	12468012	0.00	0.43	0.63
China	4060765990	Sichuan	2129623.30	4655770	0.01	0.60	0.63
China	4060780100	Sichuan	1056590.45	6680384	0.06	0.84	0.60
China	4060784170	Tibet	1177438.44	8057633	0.00	0.65	0.60
China	4060239010	Heilongjiang	2376775.51	7869675	0.00	0.23	0.59
China	4060152570	Inner Mongolia	1436314.16	13308502	0.00	0.27	0.58
South Korea	4060005050	Gangwon State	661898.98	2719734	0.25	0.88	0.86
South Korea	4060003940	North Gyeongsang	829641.01	1266503	0.09	0.18	0.64
South Korea	4060004110	South Jeolla	668018.80	1000731	0.33	0.60	0.61
South Korea	4060004690	South Chungcheong	573493.41	368187	0.04	0.62	0.60
South Korea	4060004680	North Chungcheong	289990.94	641605	0.27	0.81	0.53
South Korea	4060005050	Gyeonggi	417991.02	691925	0.45	0.80	0.53
South Korea	4060005050	North Chungcheong	272147.62	846176	0.14	0.58	0.52
South Korea	4060003940	South Gyeongsang	488110.87	696291	0.08	0.26	0.51
South Korea	4060003940	Busan	2285.72	3110	0.03	1.05	0.51
South Korea	4060004690	Seoul	1843.32	507	0.04	1.05	0.50
Thailand	4060021590	Phang-nga Province	245235.16	4624243	0.19	0.76	0.74
Thailand	4060018990	Nakhon Si Thammarat Province	505216.87	2442041	0.15	0.76	0.74
Thailand	4060019420	Yala Province	340202.95	3516882	0.15	0.72	0.72
Thailand	4060018970	Surat Thani Province	514728.40	3331241	0.37	0.68	0.71
Thailand	4060018360	Kanchanaburi Province	625769.93	3349343	0.50	0.57	0.70
Thailand	4060019420	Narathiwat Province	371096.98	3205501	0.20	0.71	0.70
Thailand	4060018230	Chachoengsao Province	371391.91	518250	0.06	0.81	0.62
Thailand	4060021590	Krabi Province	185364.16	2643575	0.12	0.69	0.61
Thailand	4060021590	Ranong Province	171354.93	2801321	0.36	0.90	0.61
Thailand	4060018230	Prachin Buri Province	330101.57	1368072	0.14	0.72	0.60
United Kingdom	2060051160	Scotland	3736112.67	54955700	0.38	0.70	0.83
United Kingdom	2060050790	Scotland	972166.44	20513463	0.12	0.75	0.56
United Kingdom	2060049690	Wales	1164535.21	25987594	0.41	0.69	0.52
United Kingdom	2060050420	England	845862.22	24518796	0.57	0.92	0.51
United Kingdom	2060053240	England	673991.16	20112092	0.42	0.90	0.51
United Kingdom	2060052960	England	842912.91	20521267	0.39	0.77	0.49
United Kingdom	2060055600	Northern Ireland	487004.88	13689548	0.23	0.78	0.48
United Kingdom	2060055310	Northern Ireland	204387.21	6189826	0.13	0.85	0.47
United Kingdom	2060051150	Scotland	323981.75	7635554	0.07	0.70	0.46
United Kingdom	2060050790	England	38193.57	852933	0.07	0.90	0.46
France	2060552470	Auvergne-Rhône-Alpes	523502.59	3630486	0.34	0.54	0.66
France	2060536370	Auvergne-Rhône-Alpes	570249.16	3036506	0.30	0.50	0.65
France	2060021230	Normandy	262488.62	5501654	0.38	0.58	0.65
France	2060021230	Brittany	684682.41	2205511	0.21	0.38	0.64
France	2060420240	Grand Est	544295.23	3653752	0.29	0.23	0.60
France	2060502920	Nouvelle-Aquitaine	557862.06	2238149	0.16	0.32	0.60
France	2060569180	Occitania	418802.08	1736915	0.28	0.62	0.57
France	2060021030	Pays de la Loire	390783.63	1268552	0.13	0.55	0.56
France	2060497340	Pays de la Loire	450064.77	2084031	0.11	0.26	0.55
France	2060510690	Bourgogne – Franche-Comté	333567.01	3552314	0.39	0.45	0.55
Spain	2060018360	Andalusia	814230.87	6299718	0.23	0.85	0.91
Spain	2060615620	Aragon	330765.16	2286648	0.28	0.67	0.54
Spain	2060609250	Aragon	172239.73	1230820	0.24	0.97	0.53
Spain	2060018240	Andalusia	378396.52	1758187	0.24	0.56	0.52
Spain	2060613120	Castile and León	366894.21	1091214	0.18	0.53	0.49
Spain	2060017700	Andalusia	392995.61	903264	0.23	0.55	0.49
Spain	2060615620	Catalonia	250986.31	3179844	0.36	0.47	0.48
Spain	2060689480	Castile-La Mancha	1032.26	7840	0.14	1.05	0.48
Spain	2060019420	Galicia	2728.11	14942	0.11	1.00	0.48
Spain	2060598480	Aragon	102046.14	976468	0.28	0.89	0.47
Brazil	6060294360	Amazonas	4883173.22	141682590	0.57	0.84	0.73
Brazil	6060269510	Amazonas	4911339.13	148601298	0.61	0.70	0.70
Brazil	6060287830	Amazonas	3724536.63	181332932	0.62	0.78	0.70
Brazil	6060286190	Amazonas	4049698.10	108787314	0.47	0.82	0.67
Brazil	6060305980	Amazonas	2601807.90	69408761	0.00	0.77	0.66
Brazil	6060267240	Amazonas	3240923.46	120317611	0.59	0.86	0.63
Brazil	6060280410	Amazonas	1969918.14	51386243	0.05	0.83	0.61
Brazil	6060360870	Amazonas	3137918.79	88687485	0.33	0.69	0.60
Brazil	6060247260	Amazonas	2710932.38	210340274	0.96	0.79	0.59
Brazil	6060692440	Mato Grosso do Sul	2165531.16	31779711	0.02	0.74	0.58
Chile	6060036450	Magallanes and Chilean Antarctica Region	2508167.29	50305952	0.29	0.63	0.84
Chile	6060024060	Aysen del General Carlos Ibanez del Campo Region	1866028.69	40006604	0.65	0.44	0.58
Chile	6060025140	Aysen del General Carlos Ibanez del Campo Region	1281032.97	29289888	0.36	0.58	0.58
Chile	6060022920	Magallanes and Chilean Antarctica Region	1907540.23	44721318	0.94	0.56	0.57
Chile	6060039730	Los Lagos Region	739834.51	11770384	0.06	0.77	0.56
Chile	6060025130	Aysen del General Carlos Ibanez del Campo Region	1090360.05	22264746	0.15	0.48	0.55
Chile	6060021950	Magallanes and Chilean Antarctica Region	1602212.87	33232005	0.73	0.56	0.53
Chile	6060026390	Los Ríos	26322.60	337804	0.00	1.05	0.52
Chile	6060038660	Magallanes and Chilean Antarctica Region	1210691.92	41324289	0.90	0.63	0.51
Chile	6060026390	Araucania Region	31852.55	438122	0.00	1.02	0.51
Australia	5060077790	Tasmania	2510379.27	95835397	0.51	0.82	0.83
Australia	5060456070	South Australia	577917.37	2485184	0.02	0.98	0.55
Australia	5060049720	Queensland	1511521.58	20521613	0.35	0.67	0.54
Australia	5060352950	Western Australia	73364.10	278040	0.00	1.05	0.52
Australia	5060405540	Western Australia	6857.15	0	0.01	1.05	0.51
Australia	5060284090	Northern Territory	4571.43	33459	0.02	1.05	0.51
Australia	5060373470	Western Australia	4202.77	641	0.02	1.05	0.51
Australia	5060590940	New South Wales	5751.16	67171	0.01	1.04	0.51
Australia	5060591290	New South Wales	24921.67	261349	0.00	1.02	0.51
Australia	5060057550	Western Australia	7963.14	55137	0.03	1.05	0.51
India	4060838850	Arunachal Pradesh	1023558.17	16138869	0.00	0.80	0.78
India	4060838850	South Tibet	988240.18	15930818	0.00	0.80	0.78
India	4060048470	Andaman and Nicobar Islands	519742.22	14082290	0.00	0.91	0.74
India	4060949680	Meghalaya	758046.50	8537129	0.00	0.93	0.68
India	4060835610	Arunachal Pradesh	835097.24	13006787	0.00	0.63	0.68
India	4060835610	South Tibet	786138.68	12501877	0.00	0.61	0.66
India	4060032880	Gujarat	1669014.75	1348407	0.00	0.76	0.60
India	4060029870	Karnataka	1233770.27	5971331	0.04	0.64	0.59
India	4060836200	Arunachal Pradesh	514802.13	7857501	0.00	0.67	0.58
India	4060836200	South Tibet	514802.13	7857501	0.00	0.67	0.58

Cross-Country Overview

Code

all_results |>
    ggplot(aes(
        x = reorder(country, composite_score, FUN = median),
        y = composite_score
    )) +
    geom_boxplot(fill = "#66c2a5", alpha = 0.7) +
    geom_jitter(width = 0.2, alpha = 0.5, size = 2) +
    coord_flip() +
    labs(
        title = "Distribution of Composite Scores: Top 10 Basins Per Country",
        subtitle = "Scores normalized within each country - not directly comparable across countries",
        x = NULL,
        y = "Composite Score (country-specific)"
    ) +
    theme_minimal() +
    theme(panel.grid.major.y = element_blank())

Figure 14: Distribution of composite scores across countries (top 10 basins per country). Note: Scores normalized within each country.

Key Findings

Methodology

For each country, we identified the top 10 Level 6 HydroBASINS based on a composite score that equally weights four key metrics:

Wetland Area (25%): Total hectares of wetlands from GLWD
Carbon Storage (25%): Vulnerable carbon in wetlands
Protection Status (25%): Fraction of wetlands within WDPA protected areas
Nature’s Contributions (25%): Average NCP biodiversity score

Interpretation

The composite score helps identify hydrobasins that balance multiple conservation priorities: - High wetland area indicates ecological significance - High carbon storage suggests climate mitigation importance - Low protection fraction highlights conservation gaps - High NCP scores indicate biodiversity value and ecosystem services

Outputs

For each country, the analysis produces:

Complete basin file (priority_hydrobasins_all_<country>.csv): All basins with wetlands, ranked by composite score
Country visualization (priority_hydrobasins_<country>.png): Four-panel figure showing:
- Top 10 basins by composite score
- Breakdown of the four conservation criteria
- Distribution of all basin scores
- Relationship between wetland area and protection status
Regional identification: Each basin labeled with its administrative region for spatial context